With the development of information technology and the ubiquity of mobile devices, increasing amounts of data are generated, processed and transmitted by mobile devices. In order to alleviate the tension between the energy poverty of mobile devices and the increasing demand for transmitting data, the energy-efficient data transmission problem attracts considerable research interests. Nonetheless, how to upload data with redundancy efficiently still lacks a thorough study in spite of the wide existence of this problem in many situations like data storage among mobile devices and mobile crowd sensing. Since uploading redundant data brings little value while still consuming precious energy, it is important to design an efficient approach for mobile devices to upload data with redundancy cooperatively. In this talk, we formulate the uploading data with redundancy in cooperative mobile cloud as an energy-constrained utility maximization problem. To solve this problem, we propose an adaptive distributed optimization approach consisting of the correlated upload decision and the online distributed scheduling algorithm. By the correlated upload decision, each mobile device can make adaptive decisions on how much data to upload and which data to upload according to its own observations independently. The online distributed scheduling algorithm enables mobile devices to optimally upload data while requiring no future information.